SEPA Identifier of the Creditor

Introduction

The function clean_eu_at_02() cleans a column containing AT-02 (SEPA Creditor identifier) strings, and standardizes them in a given format. The function validate_eu_at_02() validates either a single AT-02 strings, a column of AT-02 strings or a DataFrame of AT-02 strings, returning True if the value is valid, and False otherwise.

AT-02 strings can be converted to the following formats via the output_format parameter:

• compact: only number strings without any seperators or whitespace, like “ES23ZZZ47690558N”

• standard: AT-02 strings with proper whitespace in the proper places. Note that in the case of AT-02, the compact format is the same as the standard one.

Invalid parsing is handled with the errors parameter:

• coerce (default): invalid parsing will be set to NaN

• ignore: invalid parsing will return the input

• raise: invalid parsing will raise an exception

The following sections demonstrate the functionality of clean_eu_at_02() and validate_eu_at_02().

An example dataset containing AT-02 strings

import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "eu_at_02": [
            'ES++()+23ZZZ4//7690558N',
            'ES2900047690558N',
            'BE 428759497',
            'BE431150351',
            "002 724 334",
            "hello",
            np.nan,
            "NULL",
        ],
        "address": [
            "123 Pine Ave.",
            "main st",
            "1234 west main heights 57033",
            "apt 1 789 s maple rd manhattan",
            "robie house, 789 north main street",
            "1111 S Figueroa St, Los Angeles, CA 90015",
            "(staples center) 1111 S Figueroa St, Los Angeles",
            "hello",
        ]
    }
)
df

	eu_at_02	address
0	ES++()+23ZZZ4//7690558N	123 Pine Ave.
1	ES2900047690558N	main st
2	BE 428759497	1234 west main heights 57033
3	BE431150351	apt 1 789 s maple rd manhattan
4	002 724 334	robie house, 789 north main street
5	hello	1111 S Figueroa St, Los Angeles, CA 90015
6	NaN	(staples center) 1111 S Figueroa St, Los Angeles
7	NULL	hello

1. Default clean_eu_at_02

By default, clean_eu_at_02 will clean eu_at_02 strings and output them in the standard format with proper separators.

from dataprep.clean import clean_eu_at_02
clean_eu_at_02(df, column = "eu_at_02")

	eu_at_02	address	eu_at_02_clean
0	ES++()+23ZZZ4//7690558N	123 Pine Ave.	ES23ZZZ47690558N
1	ES2900047690558N	main st	NaN
2	BE 428759497	1234 west main heights 57033	NaN
3	BE431150351	apt 1 789 s maple rd manhattan	NaN
4	002 724 334	robie house, 789 north main street	NaN
5	hello	1111 S Figueroa St, Los Angeles, CA 90015	NaN
6	NaN	(staples center) 1111 S Figueroa St, Los Angeles	NaN
7	NULL	hello	NaN

2. Output formats

This section demonstrates the output parameter.

standard (default)

clean_eu_at_02(df, column = "eu_at_02", output_format="standard")

	eu_at_02	address	eu_at_02_clean
0	ES++()+23ZZZ4//7690558N	123 Pine Ave.	ES23ZZZ47690558N
1	ES2900047690558N	main st	NaN
2	BE 428759497	1234 west main heights 57033	NaN
3	BE431150351	apt 1 789 s maple rd manhattan	NaN
4	002 724 334	robie house, 789 north main street	NaN
5	hello	1111 S Figueroa St, Los Angeles, CA 90015	NaN
6	NaN	(staples center) 1111 S Figueroa St, Los Angeles	NaN
7	NULL	hello	NaN

compact

clean_eu_at_02(df, column = "eu_at_02", output_format="compact")

	eu_at_02	address	eu_at_02_clean
0	ES++()+23ZZZ4//7690558N	123 Pine Ave.	ES23ZZZ47690558N
1	ES2900047690558N	main st	NaN
2	BE 428759497	1234 west main heights 57033	NaN
3	BE431150351	apt 1 789 s maple rd manhattan	NaN
4	002 724 334	robie house, 789 north main street	NaN
5	hello	1111 S Figueroa St, Los Angeles, CA 90015	NaN
6	NaN	(staples center) 1111 S Figueroa St, Los Angeles	NaN
7	NULL	hello	NaN

3. inplace parameter

This deletes the given column from the returned DataFrame. A new column containing cleaned AT-02 strings is added with a title in the format "{original title}_clean".

clean_eu_at_02(df, column="eu_at_02", inplace=True)

	eu_at_02_clean	address
0	ES23ZZZ47690558N	123 Pine Ave.
1	NaN	main st
2	NaN	1234 west main heights 57033
3	NaN	apt 1 789 s maple rd manhattan
4	NaN	robie house, 789 north main street
5	NaN	1111 S Figueroa St, Los Angeles, CA 90015
6	NaN	(staples center) 1111 S Figueroa St, Los Angeles
7	NaN	hello

4. errors parameter

coerce (default)

clean_eu_at_02(df, "eu_at_02", errors="coerce")

	eu_at_02	address	eu_at_02_clean
0	ES++()+23ZZZ4//7690558N	123 Pine Ave.	ES23ZZZ47690558N
1	ES2900047690558N	main st	NaN
2	BE 428759497	1234 west main heights 57033	NaN
3	BE431150351	apt 1 789 s maple rd manhattan	NaN
4	002 724 334	robie house, 789 north main street	NaN
5	hello	1111 S Figueroa St, Los Angeles, CA 90015	NaN
6	NaN	(staples center) 1111 S Figueroa St, Los Angeles	NaN
7	NULL	hello	NaN

ignore

clean_eu_at_02(df, "eu_at_02", errors="ignore")

	eu_at_02	address	eu_at_02_clean
0	ES++()+23ZZZ4//7690558N	123 Pine Ave.	ES23ZZZ47690558N
1	ES2900047690558N	main st	ES2900047690558N
2	BE 428759497	1234 west main heights 57033	BE 428759497
3	BE431150351	apt 1 789 s maple rd manhattan	BE431150351
4	002 724 334	robie house, 789 north main street	002 724 334
5	hello	1111 S Figueroa St, Los Angeles, CA 90015	hello
6	NaN	(staples center) 1111 S Figueroa St, Los Angeles	NaN
7	NULL	hello	NaN

4. validate_eu_at_02()

validate_eu_at_02() returns True when the input is a valid AT-02. Otherwise it returns False.

The input of validate_eu_at_02() can be a string, a Pandas DataSeries, a Dask DataSeries, a Pandas DataFrame and a dask DataFrame.

When the input is a string, a Pandas DataSeries or a Dask DataSeries, user doesn’t need to specify a column name to be validated.

When the input is a Pandas DataFrame or a dask DataFrame, user can both specify or not specify a column name to be validated. If user specify the column name, validate_eu_at_02() only returns the validation result for the specified column. If user doesn’t specify the column name, validate_eu_at_02() returns the validation result for the whole DataFrame.

from dataprep.clean import validate_eu_at_02
print(validate_eu_at_02('ES++()+23ZZZ4//7690558N'))
print(validate_eu_at_02('ES2900047690558N'))
print(validate_eu_at_02('BE 428759497'))
print(validate_eu_at_02('BE431150351'))
print(validate_eu_at_02("004085616"))
print(validate_eu_at_02("hello"))
print(validate_eu_at_02(np.nan))
print(validate_eu_at_02("NULL"))

True
False
False
False
False
False
False
False

Series

validate_eu_at_02(df["eu_at_02"])

0     True
1    False
2    False
3    False
4    False
5    False
6    False
7    False
Name: eu_at_02, dtype: bool

DataFrame + Specify Column

validate_eu_at_02(df, column="eu_at_02")

0     True
1    False
2    False
3    False
4    False
5    False
6    False
7    False
Name: eu_at_02, dtype: bool

Only DataFrame

validate_eu_at_02(df)

	eu_at_02	address
0	True	False
1	False	False
2	False	False
3	False	False
4	False	False
5	False	False
6	False	False
7	False	False